The present invention relates generally to monitoring traffic flow conditions on a road system, and more specifically to collecting, processing and managing traffic data to determine real-time traffic conditions on a roadway using a virtual traffic network.
Collecting and disseminating information related to traffic flow on a road system is generally known in the art. FIG. 1 illustrates an example of a typical traffic collection and reporting system 100. Information about traffic flow is often collected through live descriptions of the road system (or portions thereof) from aircraft or mobile units, traffic operators 102 listening to scanners, or other similar means. One commonly used method of collecting traffic flow information usually entails describing traffic information in free form text, and inputting the text to a PC or similar application 104 specifically designed for collecting a free-form text description of traffic information. The traffic information is typically displayed as a group of text messages 106, and is often copied or faxed to a media house (not shown), such as a radio or TV station, for on-air reporting. Since the collected traffic information as reported by the media house is not substantively processed, it has no value other then the text description itself. A recent improvement in reporting traffic information includes delivering the traffic information directly to the media house via direct network connections. However, the traffic information is still reported in the same text message format 106. FIG. 2 shows an enlarged version of the text message format 106 according to the prior art.
The traffic industry has long struggled with the problem of being able to determine, with any degree of accuracy and consistency, the details and lifecycle of traffic events that occur along major roadways. The details (such as the number of and which lanes are affected, the presence of emergency personnel on scene, road closure, shoulder passage, etc.) are important in understanding the impact the traffic event will have on roadway conditions, both immediately and over time. The ability to accurately describe an event in detail in a consistent and standard manner, track the progress of the event over time, accurately locate the event on a geo-spatial traffic network to determine its relative locations to other events and roadways, and understand the impact of the event on traffic flow, all in real-time, has never been accurately solved. Without this level of detail it is impossible to accurately correlate factors such as multiple traffic events on the same or different highways, conditions at the time of the event, or the predicted clearance time of the event.
FIG. 1 further shows another well known method of collecting and reporting traffic data through the use of digital roadside sensors 115 (either within the roadway such as loops or within the right of way on free standing structures). Various types of sensors 115 known in the art are employed for this purpose. The digital sensors 115 collect traffic flow data 116 such as speed, volume (number of vehicles passing the sensor per period of time), vehicle classification (car or truck), and density (the percentage of the roadway that is occupied with vehicles). The traffic flow data 116 is generally collected in real-time (as it occurs), and then input to a computer system 103 where the flow data 116 is analyzed and converted into traffic data which is integrated with commercial map data 108.
However, the traffic industry has been unable to integrate real-time traffic flow data 116 on a lane-by-lane basis into advanced traffic event collection systems. Existing traffic event collection systems, such as PB Farradyne's MIST® software platform, California's CALTRANS, and GCM Gateway developed by the Illinois Dept. of Transportation, collect flow data in real-time but do not integrate that data into event collection systems that accurately provide point to point traffic data (such as congestion information) on a geo-located road network and which reflects the impact of other traffic events in the system. The most advanced systems provide color-coded traffic flow reflecting analysis of the traffic flow data 116, typically in the form of a web page showing a graphical representation 105 of the road system. FIG. 3 shows an enlarged version of the graphical representation 105 of the traffic flow on the road system. Such a system does not report traffic data in real-time nor does the traffic data reflect traffic events on the road system. A few traffic collection systems provide actual travel times along major corridors and roadways. However, such traffic data is also not integrated with a traffic event collection system. Even though some public agencies and a few private companies display a map with traffic events combined with traffic flow data 116 on a web page or computer application 107, the resulting traffic data is discrete and displayed on the road network map from a rendering perspective. That is, the traffic events have no knowledge of the flow data on the map and vice versa, and their impact on each other is unknown—each piece of data is placed on the map independently of the other data. The traffic event information and the flow data are treated as separate systems even though they are placed on the same road network map from the users' perspective. Additionally, since the exact location of an event is never mapped onto a geo-spatial traffic network, there is never an understanding of how one traffic event impacts the overall flow of traffic. FIG. 4 shows an enlarged version of a web page or computer application 107 showing combined traffic flow data 116 with traffic events according to the prior art.
Without integrating real-time traffic flow data 116 with event data, the true impact that a traffic event has on roadway conditions is impossible to determine. For example, accurate traffic data, such as travel time and delay time cannot be determined. Additionally, the geo-location of congestion on a road system, the queuing effect the congestion is causing, the determination of travel time through that congestion and the dissemination of that information in real-time is impossible.
Problems also persist with the state of the art of reporting traffic data. First, there is no parameterization of individual traffic events so that specific changes in the lifecycle of a traffic event may be tracked. Additionally, when reporting traffic information via free form-text there is no consistent format from report to report. For example, if a lane is closed, a subsequent traffic report may or may not include the state of the lane. Furthermore, since there are no set parameters, the traffic data cannot be tracked, stored and compared historically. Thus, if a similar event previously occurred on the same or different roadway, there is no ability to view detailed information of a prior, similar event to compare various traffic data, such as clearance time, to help predict clearance time for the present traffic event.
Present traffic systems also cannot store real-time flow data and event information in a “warehouse” so that true data mining against both the flow data and event data can be achieved for use in real-time advanced algorithms. As such, there is no national database of traffic data that integrates real-time flow and event data that can be mined either for a particular city or across multiple metropolitan areas, up to and including a national view.
Because of the manner in which traffic data is currently reported, there is no system which not only integrates traffic event information and traffic flow data, but also provides an interface so that different applications can easily retrieve the traffic data in a format that which is suitable for multiple media applications, such as radio and television. There is also no ability to fully qualify the flow data together with event data on a spatially correct road system which allows applications to retrieve traffic data in a personalized and granular way.
Additionally, present traffic collection and reporting systems do not utilize a layered architecture that enables collection of disparate types of sensor data for integration into a common platform with event data which, on the user side, provides a common component architecture to allow for seamless integration of various renderings in multiple applications for the government, telematics, fleet/logistics, the media, and consumers. There is no layered architecture that allows end-user applications to leverage a common component interface so that multiple renderings of the same data can be easily manipulated and multiple traffic reporting applications can be developed without altering the core traffic data processing and management system.